A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)-based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA-IPP method performs better than state-of-the-art planners, and we demonstrate its application in a realistic urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and Automation (ICRA-2019) to be held at Montreal, Canad

Routing Unmanned Vehicles in GPS-Denied Environments

Most of the routing algorithms for unmanned vehicles, that arise in data gathering and monitoring applications in the literature, rely on the Global Positioning System (GPS) information for localization. However, disruption of GPS signals either intentionally or unintentionally could potentially render these algorithms not applicable. In this article, we present a novel method to address this difficulty by combining methods from cooperative localization and routing. In particular, the article formulates a fundamental combinatorial optimization problem to plan routes for an unmanned vehicle in a GPS-restricted environment while enabling localization for the vehicle. We also develop algorithms to compute optimal paths for the vehicle using the proposed formulation. Extensive simulation results are also presented to corroborate the effectiveness and performance of the proposed formulation and algorithms.Comment: Publised in International Conference on Umanned Aerial System

Autonomous Approach and Landing Algorithms for Unmanned Aerial Vehicles

In recent years, several research activities have been developed in order to increase the autonomy features in Unmanned Aerial Vehicles (UAVs), to substitute human pilots in dangerous missions or simply in order to execute specific tasks more efficiently and cheaply. In particular, a significant research effort has been devoted to achieve high automation in the landing phase, so as to allow the landing of an aircraft without human intervention, also in presence of severe environmental disturbances. The worldwide research community agrees with the opportunity of the dual use of UAVs (for both military and civil purposes), for this reason it is very important to make the UAVs and their autolanding systems compliant with the actual and future rules and with the procedures regarding autonomous flight in ATM (Air Traffic Management) airspace in addition to the typical military aims of minimizing fuel, space or other important parameters during each autonomous task. Developing autolanding systems with a desired level of reliability, accuracy and safety involves an evolution of all the subsystems related to the guide, navigation and control disciplines. The main drawbacks of the autolanding systems available at the state of art concern or the lack of adaptivity of the trajectory generation and tracking to unpredicted external events, such as varied environmental condition and unexpected threats to avoid, or the missed compliance with the guide lines imposed by certification authorities of the proposed technologies used to get the desired above mentioned adaptivity. During his PhD period the author contributed to the development of an autonomous approach and landing system considering all the indispensable functionalities like: mission automation logic, runway data managing, sensor fusion for optimal estimation of vehicle state, trajectory generation and tracking considering optimality criteria, health management algorithms. In particular the system addressed in this thesis is capable to perform a fully adaptive autonomous landing starting from any point of the three dimensional space. The main novel feature of this algorithm is that it generates on line, with a desired updating rate or at a specified event, the nominal trajectory for the aircraft, based on the actual state of the vehicle and on the desired state at touch down point. Main features of the autolanding system based on the implementation of the proposed algorithm are: on line trajectory re-planning in the landing phase, fully autonomy from remote pilot inputs, weakly instrumented landing runway (without ILS availability), ability to land starting from any point in the space and autonomous management of failures and/or adverse atmospheric conditions, decision-making logic evaluation for key-decisions regarding possible execution of altitude recovery manoeuvre based on the Differential GPS integrity signal and compatible with the functionalities made available by the future GNSS system. All the algorithms developed allow reducing computational tractability of trajectory generation and tracking problems so as to be suitable for real time implementation and to still obtain a feasible (for the vehicle) robust and adaptive trajectory for the UAV. All the activities related to the current study have been conducted at CIRA (Italian Aerospace Research Center) in the framework of the aeronautical TECVOL project whose aim is to develop innovative technologies for the autonomous flight. The autolanding system was developed by the TECVOL team and the author’s contribution to it will be outlined in the thesis. Effectiveness of proposed algorithms has been then evaluated in real flight experiments, using the aeronautical flying demonstrator available at CIRA